Abstract
With the effects of climate change, a growing human population, and the need to ensurefood security, understanding the development of plants is a crucial part of the possible solutions. This study is considered to be highly significant in addressing these pressing concerns. To undertake such a study, it is necessary to explore how genetic variability gives rise to specific sets of architectural traits. Understanding these architectural variations facilitates the regulation, prediction and assurance of fruit production. It will also allow management strategies to be proposed to maintain the health of the tree and optimize its productivity. Fruit trees typically consist of two components: the upper part, including the trunk, branches, and other organs above ground, and the underground part encompassing the entire root system. In both cases, studying the architecture raises various questions concerning tree geometry, the genes involved in tree development and variation, and the successive states of the tree. By addressing these questions, understanding the architecture of fruit trees can contribute to the improvement of varieties and their production. Hence, several techniques have been developed to measure tree organs and model their architecture and shape. Initially, this task required a group of human operators to physically measure these objects directly in the field. However, this approach is time-consuming and demands extensive observation. Over the past few decades, the advent of new sensors such as cameras and LiDAR (Light Detection and Ranging) has facilitated the acquisition of digital representations of trees, opening up new possibilities for study. The objective of this thesis is to obtain architectural metrics that provide an overall perspective of the tree’s geometry, followed by a more precise approximation at the organ level. These metrics were based on the processing of point clouds obtained from terrestrial and aerial LiDAR in an apple orchard including a collection of genotypes. The point clouds were processed by developing two pipelines utilizing various filtering, machine learning, and deep learning algorithms.